by João Medeiros, Head of AI at AI Collaborator

In the fast-paced world of modern business, C-suite executives understand the critical importance of data infrastructure and data science practices. To capitalize on these investments and their impact on the bottom line, it’s essential to make data-driven decisions that resonate across the entire enterprise.

Questions inevitably arise: What’s the best approach for making these data-driven decisions? Can these investments enhance competitiveness, customer satisfaction, speed to market, and other business priorities? The key lies in understanding causal relationships within your data.

Delta Airlines: A Case for Causal Inference

Consider Delta Airlines, a prominent player in the airline industry. Their commitment to a data-driven approach is evident, with investments in AI innovation. However, the SkyMiles program incident of 2023, where pricing changes led to consumer backlash, highlights an opportunity for a more robust approach to connecting leadership with data.

Understanding Causal Inference

Causal inference, an area of statistics, provides the framework for focusing your company’s efforts on what truly impacts outcomes like revenue, reputation, and customer satisfaction. It differentiates true impacts from mere correlations, offering measures of causal strength, surpassing mere correlation in significance and predictive power. Thus, causal modeling outperforms traditional Machine Learning approaches, where causally unrelated features and target data are fed into models, producing low-performance results and inefficient data pipelines in production environments.

In the Delta SkyMiles example, a firm grasp of causal relationships could have prevented the errant pricing decision. In a causal framework, all business data relationships must be understood, with the most crucial continually monitored.  Machine learning algorithms for causal structure discovery are the ideal tools for estimating causal strength between variables. They provide a transparent foundation for data science, understandable by non-technical stakeholders.

Harnessing Causal Inference for Business Success

Causal inference empowers business decision-makers to address issues at their root. It helps uncover why your business faces challenges and identifies strategies for revenue enhancement and risk mitigation.

In essence, causal analysis allows you to explicitly determine the relationships between variables in your data. It provides a solid foundation for data-driven decision-making that can be applied to any industry. By deploying causal analysis, you will have the opportunity to understand your data better and, in doing so, uncover the root cause of your success.

Are you looking to upgrade your decision-making with a causal approach to data analysis? Want to discover the full potential of your data and make better decisions? Contact our VP of Sales at to learn how embracing a causal approach could mean your CEO may never have to say sorry again.